45 citations · 54 across the 11 of their papers we have counts for
11 papers
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement
Yuting Yang, Pei Huang, Juan Cao +5
Recent years have seen the wide application of NLP models in crucial areas such as finance, medical treatment, and news media, raising concerns of the model robustness and vulnerab…
Quantifying Robustness to Adversarial Word Substitutions
Yuting Yang, Pei Huang, FeiFei Ma +4
Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations. Before they are widely adopted, the fundamental issues of robustness need to be addres…
Can Graph Neural Networks Learn to Solve MaxSAT Problem?
Minghao Liu, Fuqi Jia, Pei Huang +5
With the rapid development of deep learning techniques, various recent work has tried to apply graph neural networks (GNNs) to solve NP-hard problems such as Boolean Satisfiability…
ε-weakened Robustness of Deep Neural Networks
Pei Huang, Yuting Yang, Minghao Liu +3
This paper introduces a notation of -weakened robustness for analyzing the reliability and stability of deep neural networks (DNNs). Unlike the conventional robustness…
Counting the Number of Solutions to Constraints
Jian Zhang, Cunjing Ge, Feifei Ma
Compared with constraint satisfaction problems, counting problems have received less attention. In this paper, we survey research works on the problems of counting the number of so…
ACFNet: Attentional Class Feature Network for Semantic Segmentation
Fan Zhang, Yanqin Chen, Zhihang Li +5
Recent works have made great progress in semantic segmentation by exploiting richer context, most of which are designed from a spatial perspective. In contrast to previous works, w…